add keyword indexing + constructor

src/core.jl

@@ -235,6 +235,11 @@ function AxisArray(A::AbstractArray{T,N}, names::NTuple{N,Symbol}, steps::NTuple
     AxisArray(A, axs...)
 end
 
+# Alternative constructor, takes names as keywords:
+AxisArray(A; kw...) = AxisArray(A, nt_to_axes(kw.data)...)
+@generated nt_to_axes(nt::NamedTuple) =
+    Expr(:tuple, (:(Axis{$(QuoteNode(n))}(getfield(nt, $(QuoteNode(n))))) for n in nt.names)...)
+
 AxisArray(A::AxisArray) = A
 AxisArray(A::AxisArray, ax::Vararg{Axis, N}) where N =
     AxisArray(A.data, ax..., last(Base.IteratorsMD.split(axes(A), Val(N)))...)

src/indexing.jl

@@ -129,7 +129,6 @@ end
 @propagate_inbounds getindex_converted(A, idxs...) = A.data[idxs...]
 @propagate_inbounds setindex!_converted(A, v, idxs...) = (A.data[idxs...] = v)
 
-
 # First is indexing by named axis. We simply sort the axes and re-dispatch.
 # When indexing by named axis the shapes of omitted dimensions are preserved
 # TODO: should we handle multidimensional Axis indexes? It could be interpreted
@@ -155,6 +154,19 @@ function Base.reshape(A::AxisArray, ::Val{N}) where N
     AxisArray(reshape(A.data, Val(N)), Base.front(axN))
 end
 
+# Keyword indexing, reconstructs the Axis{}() objects
+@propagate_inbounds Base.view(A::AxisArray; kw...) =
+    view(A, kw_to_axes(parent(A), kw.data)...)
+@propagate_inbounds Base.getindex(A::AxisArray; kw...) =
+    getindex(A, kw_to_axes(parent(A), kw.data)...)
+@propagate_inbounds Base.setindex!(A::AxisArray, val; kw...) =
+    setindex!(A, val, kw_to_axes(parent(A), kw.data)...)
+
+function kw_to_axes(A::AbstractArray, nt::NamedTuple)
+    length(nt) == 0 && throw(BoundsError(A, ())) # Trivial case A[] lands here
+    nt_to_axes(nt)
+end
+
 ### Indexing along values of the axes ###
 
 # Default axes indexing throws an error

test/core.jl

@@ -132,6 +132,11 @@ B = AxisArray([1 4; 2 5; 3 6], (:x, :y), (0.2, 100))
 B = AxisArray([1 4; 2 5; 3 6], (:x, :y), (0.2, 100), (-3,14))
 @test axisnames(B) == (:x, :y)
 @test axisvalues(B) == (-3:0.2:-2.6, 14:100:114)
+# Keyword constructor
+C = AxisArray([1 4; 2 5; 3 6], x=10:10:30, y=[:a, :b])
+@test axisnames(C) == (:x, :y)
+@test axisvalues(C) == (10:10:30, [:a, :b])
+@test @inferred(AxisArray(parent(C), x=1:3, y=1:2)) isa AxisArray
 
 @test AxisArrays.HasAxes(A)   == AxisArrays.HasAxes{true}()
 @test AxisArrays.HasAxes([1]) == AxisArrays.HasAxes{false}()

test/indexing.jl

@@ -302,3 +302,12 @@ a = [2, 3, 7]
 @test a[idx] ≈ 6.2
 aa = AxisArray(a, :x)
 @test aa[idx] ≈ 6.2
+
+# Keyword indexing
+A = AxisArray([1 2; 3 4], Axis{:x}(10:10:20), Axis{:y}(["c", "d"]))
+@test @inferred(A[x=1, y=1]) == 1
+@test @inferred(A[x=1]) == [1, 2]
+@test axisnames(A[x=1]) == (:y,)
+@test @inferred(view(A, x=1)) == [1,2]
+@test parent(view(A, x=1)) isa SubArray
+@test @inferred(A[x=atvalue(20), y=atvalue("d")]) == 4